Writer's CEO claims enterprises are tired of the high costs and lack of control associated with "frontier" models from big labs. The market is shifting towards purpose-built, sovereign AI solutions that deliver reliable performance at a lower cost, creating an opening for specialized providers.

As AI moves from being a simple tool to an autonomous agent, pricing models are evolving. Companies like Sierra, chaired by OpenAI's Brett Taylor, advocate for outcome-based pricing, which charges for delivered results (e.g., a completed report) rather than the underlying token consumption.

AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.

The rise of AI services companies like Invisible and Palantir, which build custom on-prem solutions, marks a reversal of the standardized cloud SaaS trend. Enterprises now prioritize proprietary, custom AI applications to gain a competitive edge.

Palantir argues that enterprises going directly to LLM providers like OpenAI face high costs and vendor lock-in. Its strategy is to act as an intermediary, building custom, model-agnostic applications on client data, promising better business outcomes despite its own premium price tag.

The rise of Forward Deployed Engineers (FDEs) at OpenAI and Google isn't just about a new job title. It's a strategic Trojan horse to bypass traditional consulting firms and directly capture the massive services revenue associated with AI implementation, shifting from software sales to outcome-based pricing.

Palantir rebrands consultants as 'forward deployed engineers' and 'AI sommeliers' who provide high-touch customization for large enterprises. This reframing highlights a key market reality: even powerful AI platforms require significant human expertise to deliver value in complex environments.

Seat-based and token-based pricing for AI are intermediate steps. The ultimate model is outcome-based, where customers pay for specific value delivered—like a successful investment idea or a completed report. This perfectly aligns vendor cost with customer value, bypassing debates over token consumption ROI.

The high-margin, pure Software-as-a-Service model is becoming obsolete in the AI era. Complex AI implementation requires hands-on integration, giving rise to consultative models like the "forward deployed engineer," where provider experts are embedded with clients to ensure success.